Commonly Used Therapeutics Associated with Changes in Arousal Inhibit GABAAR Activation

GABAA receptor-positive modulators are well-known to induce sedation, sleep, and general anesthesia. Conversely, GABAA receptor negative allosteric modulators (GABAARNAMs) can increase arousal and induce seizures. Motivated by our studies with patients with hypersomnia, and our discovery that two GABAARNAMs can restore the Excitation/Inhibition (E/I) balance in vitro and arousal in vivo, we chose to screen 11 compounds that have been reported to modulate arousal, to see if they shared a GABA-related mechanism. We determined modulation with both conventional and microfluidic patch clamp methods. We found that receptor activation was variably modulated by all 11 compounds: Rifampicin (RIF), Metronidazole (MET), Minocycline (MIN), Erythromycin (ERY), Ofloxacin (OFX), Chloroquine (CQ), Hydroxychloroquine sulfate (HCQ), Flumazenil (FLZ), Pentylenetetrazol (PTZ), (-)-Epigallocatechin Gallate (EGCG), and clarithromycin (CLR). The computational modeling of modulator–receptor interactions predicted drug action at canonical binding sites and novel orphan sites on the receptor. Our findings suggest that multiple avenues of investigation are now open to investigate large and brain-penetrant molecules for the treatment of patients with diminished CNS E/I balance.


Introduction
Synaptic GABA A receptors (GABA A Rs) are the most common fast inhibitory ligandgated ion channels in the mammalian central nervous system. The receptors are expressed throughout, with differing spatial and temporal patterns [1]. The heterogeneity of subunit composition and anatomic location bestow divergent patterns of inhibition to various brain regions at different times of development [2]. These patterns of inhibition play a key role in E/I balance. Inherited and de novo mutations that alter receptor function lead to changes in the strength or duration of inhibition, and are associated with many neurologic and psychiatric diseases [3].
The endogenous enhancement of receptor function by steroids and naturally occurring peptides leads to impaired arousal [4,5]. The GABA A receptor is also a key therapeutic target for many critical therapeutics and clinically useful drugs. Benzodiazepines, Z-drugs, and many general anesthetics all exert their useful actions by positively modulating the GABA A receptors and mimicking endogenous modulation, resulting in sedation and unconsciousness [6].

Microfluidic Patch Clamp
Microfluidic electrophysiology: Intracellular saline solutions contained 145 CsCl, 2 CaCl 2 , 2 mM MgCl 2 , 10 mM EGTA, and 10 mM HEPES. Extracellular saline solutions contained 140 mM NaCl, 5 mM KCl, 2 mM CaCl 2 , 1 mM MgCl 2 , 10 mM HEPES, and 10 mM D-glucose. Both salines were adjusted to pH 7.4 (330 mOsm) and were 0.2 µm filter sterilized. The composition was based on solutions designed for long duration HEK293 biophysics studies [31]. HEK293 cells stably expressing mouse α1β2γ2L receptors [30] were dissociated with detachin (Genlantis, San Diego, CA, USA), resuspended in extracellular saline (2.5 × 10 6 cells/mL), and agitated prior to loading into 384-well Fluxion ensemble plates. In our hands, these cells can be passaged 3-40 times and still produce reproducible responses to GABA for over 2 years. In addition, we have found that electrophysiology experiments using 30 s episodes are stable and free of rundown for over 45 min. GABA concentration response relationships indicate that 100 µM GABA elicited >90 % of the maximal response.
Cells were patched using an Ionflux Mercury microfluidic automated patch clamp system [32,33] (Fluxion Biosciences, Oakland, CA, USA). Each experiment ran on a disposable 384-well plate with microfluidic channels printed into an ionized polydimethylsiloxane base. Each plate was divided into 4 zones, each containing 8 repeats of the same experimental pattern (see Figure 1). Each pattern comprised 8 compound wells containing the ligands/modulator combinations to be used in the experiment. Connected to these by a manifold perfusion chamber was an inlet well containing the cell suspension, an outlet well for discarded perfusate, and 2 trapping wells. Both trapping wells contained intracellular solution and were connected to the manifold via 20 1 µm diameter holes for cell attachment (each pattern can patch up to 40 cells simultaneously). Intracellular electrodes were placed in the 2 trapping wells, and the reference electrode in the waste well. Electrodes were connected to 64 conventional low-noise resistive feedback amplifiers. After cleaning, the plates were loaded with up to 100 µL of experimental solution or cell suspension per well. Each plate was loaded into the Fluxion Ionflux Mercury device, and a bespoke script primed the experimental fluidic channels and manifold, and the traps and sealed cells for voltage clamp measurements, and finally, controlled solution application/concentration jumps prior to saving the data collected. Agonist applications were repeated in triplicate (see Supplemental Figure S1). The current data were recorded at 10 kHz and stored for offline analysis. MATLAB scripts were used to measure the baseline leak current and peak response for each episode of data (typically 2000-4000/experiment). The matrix of peaks was expressed as a percentage of the response to a reference concentration. For the agonist concentration-response experiments, this was the maximum response. For the modulator concentration-response experiments, this was the EC 10 agonist response.
cell attachment (each pattern can patch up to 40 cells simultaneously). Intracellular electrodes were placed in the 2 trapping wells, and the reference electrode in the waste well. Electrodes were connected to 64 conventional low-noise resistive feedback amplifiers. After cleaning, the plates were loaded with up to 100 µL of experimental solution or cell suspension per well. Each plate was loaded into the Fluxion Ionflux Mercury device, and a bespoke script primed the experimental fluidic channels and manifold, and the traps and sealed cells for voltage clamp measurements, and finally, controlled solution application/concentration jumps prior to saving the data collected. Agonist applications were repeated in triplicate (see Supplemental Figure S1). The current data were recorded at 10 kHz and stored for offline analysis. MATLAB scripts were used to measure the baseline leak current and peak response for each episode of data (typically 2000-4000/experiment). The matrix of peaks was expressed as a percentage of the response to a reference concentration. For the agonist concentration-response experiments, this was the maximum response. For the modulator concentration-response experiments, this was the EC10 agonist response.

In Silico Modeling Methods
The published cryo-EM structure (PDB: 6DW0) of the GABA A receptor in complex with GABA was used as a starting template [34]. The 6DW0 was prepared using a protein preparation wizard (Schrödinger, New York, NY, USA), where the bond orders were assigned and the structure was minimized. Each of the structures: chloroquine, erythromycin, metronidazole, minocycline, ofloxacin, (-)-epigallocatechin gallate, flumazenil, hydroxychloroquine, pentylenetetrazol, clarithromycin, and rifampicin, were built and optimized in a Spartan '20 Parallel Suite (Wavefunction, Irvine, CA, USA). Tautomers were generated for each ligand, and the dominant ionization states of acidic and basic functional groups were used, assuming pH 7.4 (Schrödinger, New York, NY, USA). QikProp was used to calculate the ADME properties of the 11 ligands tested (Schrödinger, New York, NY, USA). A receptor grid generator was used to place 10 Å × 10 Å × 10 Å boxes covering the whole receptor (Schrödinger, New York, NY, USA). All 11 ligands were run for each of the grid boxes generated in Glide SP, using flexible docking with enhanced

In Silico Modeling Methods
The published cryo-EM structure (PDB: 6DW0) of the GABA A receptor in complex with GABA was used as a starting template [34]. The 6DW0 was prepared using a protein preparation wizard (Schrödinger, New York, NY, USA), where the bond orders were assigned and the structure was minimized. Each of the structures: chloroquine, erythromycin, metronidazole, minocycline, ofloxacin, (-)-epigallocatechin gallate, flumazenil, hydroxychloroquine, pentylenetetrazol, clarithromycin, and rifampicin, were built and optimized in a Spartan '20 Parallel Suite (Wavefunction, Irvine, CA, USA). Tautomers were generated for each ligand, and the dominant ionization states of acidic and basic functional groups were used, assuming pH 7.4 (Schrödinger, New York, NY, USA). QikProp was used to calculate the ADME properties of the 11 ligands tested (Schrödinger, New York, NY, USA). A receptor grid generator was used to place 10 Å × 10 Å × 10 Å boxes covering the whole receptor (Schrödinger, New York, NY, USA). All 11 ligands were run for each of the grid boxes generated in Glide SP, using flexible docking with enhanced conformational sampling by four-fold, and an expanded sampling for the selection of initial poses (Schrödinger, New York, NY, USA). The binding site for each ligand was chosen based on the most negative glide score. Erythromycin was not found to bind to any of the sites, and thus it was not carried forward. For each ligand-receptor complex, a minimization was run to reduce steric clashing between the ligand-receptor complex (Schrödinger, New York, NY, USA) using the VGSB solvation model. MMGBSA was run on each ligand-receptor complex to determine their relative affinities.

Electrophysiology
First, guided by our clinical experience, we investigated whether clarithromycin could reverse the CSF-induced enhancement of the GABA receptor function, as we have previously shown with flumazenil [7]. Using a conventional single electrode patch clamp, we found that CSF from hypersomnolent patients enhanced GABA by 89 ± 11% (n = 6 cells). Clarithromycin reversed this potentiation by 71 ± 13% (n = 6 cells, see Figure 2a). However, unlike flumazenil, we observed that clarithromycin was also able to inhibit GABA A receptor activation (Figure 2b), revealing that unlike flumazenil's benzodiazepine antagonist actions, clarithromycin acts as a GABA A receptor negative allosteric modulator or a GABA antagonist. Next, we chose to replicate this finding using a high throughput microfluidic patch clamp assay. We found this technique readily reproduced our previous finding, validating it as a suitable replacement for the traditional low-throughput technique. Using the ion flux system, we determined that clarithromycin reversibly inhibited GABA A receptor activation in a concentration-dependent manner up to 300 µM (see Figure 3). Next, we sought to further validate our microfluidic method using the well-established GABA A RNAM pentylenetetrazole (PTZ). Again, we were able to demonstrate that this method was well-suited to measuring GABA A receptor modulation, and reproduced previous findings, showing that PTZ blocked GABA A R activation in a concentration-dependent manner (see Figure 4 and Table 1). Next, we determined the effect of ofloxacin on GABA A R activation using both electrophysiologic techniques. In both cases, we found that ofloxacin produced a modest and incomplete block of receptor activation (see Figure 5 and Table 1). Similarly, we found that chloroquine and hydroxychloroqine exhibited a modest and incomplete block of receptor activation at the highest concentrations used (see Figure 6 and Table 1). Finally, using only the microfluidic method, we similarly found that metronidazole and EGCG produced a modest inhibition of GABA activation (see Figures 7 and 8 and  Table 1).         binding pocket is predicted to be defined by the lumen-facing residues of the BAE chains (βαβ subunits). (d) The rifampicin binding pocket is predicted to be defined by lumen-facing residues of the BAED chains (βαβγ subunits). Color key: α subunit (red and grey), β subunit (yellow and orange), γ subunit (blue). The hydroxychloroquine binding pocket is predicted to be defined by the β (loops D, E, and F) and γ (loops A, B, and C) subunits. Color key: α subunit (red and grey), β subunit (yellow and orange), γ subunit (blue).

Modeling
First, for our docking studies, we did not preselect any candidate binding sites or pockets, nor did we bias our results by choosing specific binding sites for each compound. Instead, we used the comprehensive method described above to find the best score for each ligand-receptor pair throughout the whole receptor. To validate our methodology, we first docked flumazenil to the 6DW0.PDB GABA A receptor structure. We were pleased when our method predicted that flumazenil binds to the α + γ − canonical benzodiazepine binding site, where it is known to interact [45], validating our approach (see Supplemental Figure S2).
Next, our docking studies predicted that the 8/9 of molecules under investigation could bind one of four distinct binding pockets in the 6DW0.pdb model of the rat α1β1γ2s receptor. The only exception was erythromycin, which failed to interact with the model. We found that none of the compounds located into either of the two canonical GABA β + α − binding sites, or any of the transmembrane anesthetic binding pockets. Our docking studies predicted that chloroquine, like flumazenil, can locate in the α + γ − benzodiazepine binding site (see Figure 6c). Next, we found that ofloxacin and rifampicin can bind to the central axial extracellular lumen between the five subunits (see Figure 5c,d). Clarithromycin preferentially docks to the α + β − cavity that until recently was thought to be an orphan homologue of the two β + α − GABA binding sites (see Figure 3c). However, this view has recently been challenged, and this site has been proposed to be a third GABA binding site [34]. We found that EGCG also docked at this interface, but closer to the transmembrane region where it can interact with residues in the cys-loop and the TM23 linker. Finally, the orphan binding pocket at the γ + β − interface is predicted to interact with MIN, HCQ, MET, and PTZ (see Figures 6d, 7c and 4c). This latter finding is particularly interesting, since despite its long clinical use and known interactions with GABA A receptors, no binding site has been proposed for PTZ. The in silico modeling parameters determined in this study are shown in Table 2, and the different docking site locations are summarized in Figure 9. study are shown in Table 2, and the different docking site locations are summarized in Figure 9. Figure 9. Cartoon of the predicted binding pockets of the compounds used in this study on the GABAAR extracellular surface, as viewed from a synapse. No modulators were predicted to act at the 2 canonical (αβ) GABA binding sites. Clarithromycin and EGCG are predicted to act at the βα GABA binding site. Flumazenil and chloroquine both docked at the canonical benzodiazepine γα binding site. Minocycline (data not shown), metronidazol, hydroxychloroquine, and pentylenetetrazole were all predicted to interact with the orphan βγ binding site. Finally, ofloxacin and rifampicin were docked in the extracellular lumen.  Figure 9. Cartoon of the predicted binding pockets of the compounds used in this study on the GABA A R extracellular surface, as viewed from a synapse. No modulators were predicted to act at the 2 canonical (αβ) GABA binding sites. Clarithromycin and EGCG are predicted to act at the βα GABA binding site. Flumazenil and chloroquine both docked at the canonical benzodiazepine γα binding site. Minocycline (data not shown), metronidazol, hydroxychloroquine, and pentylenetetrazole were all predicted to interact with the orphan βγ binding site. Finally, ofloxacin and rifampicin were docked in the extracellular lumen.

Discussion
Our conventional and microfluidic electrophysiology experiments demonstrated that clarithromycin reduces GABA A R activation and CSF-mediated potentiation. Our modeling studies suggest that it interacts with a distinct pocket on the receptor, previously thought to be an orphan site in GABA A Rs. Recently, the α + β − interface has been shown to contain electron density at saturating GABA concentrations that could order water, but more likely, a natural ligand [34]. If clarithromycin also binds at this location, it could act as a competitive GABA antagonist. If this is the case, we hypothesize that increasing the concentrations of clarithromycin would reduce the apparent affinity of the receptor for GABA limitlessly (or until the clarithromycin concentration reaches its solubility limit). Conversely, noncompetitive inhibition would produce a limited reduction in affinity, with receptor activation tending towards a finite GABA EC 50 . To test this hypothesis, we constructed GABA concentration-function relationships in the presence of 0-300 µM clarithromycin. The effect of clarithromycin concentration on GABA EC 50 is shown in Figure 10. Even as the clarithromycin concentration tended towards its aqueous solubility limit, the GABA EC 50 continued to increase. This reduction in apparent affinity suggests a competitive mechanism for clarithromycin, and support for the hypothesis that the α + β − pocket constitutes a third GABA binding site. with receptor activation tending towards a finite GABA EC50. To test this hypothesis, we constructed GABA concentration-function relationships in the presence of 0-300 μM clarithromycin. The effect of clarithromycin concentration on GABA EC50 is shown in Figure  10. Even as the clarithromycin concentration tended towards its aqueous solubility limit, the GABA EC50 continued to increase. This reduction in apparent affinity suggests a competitive mechanism for clarithromycin, and support for the hypothesis that the α + β − pocket constitutes a third GABA binding site. Figure 10. Increasing clarithromycin concentration raises the GABA EC50 exponentially. The GABA EC50 was determined at 6 different clarithromycin concentrations (0-300 μM). The increase in EC50 for each clarithromycin concentration is plotted as a decrease in apparent affinity.
Our data suggest that the chalate EGCG inhibits GABA activation and shares the α + β − GABA-binding pocket. Campbell et al. have also shown that EGCG blocks GABA activation [28]. In their detailed study, they show that the block is underpinned by an increase in the GABA EC50. Taking these observations together, we hypothesize that the α + β − interface contains a functional GABA binding site that can be targeted by the competitive antagonists clarithromycin and EGCG. The GABA antagonist actions of these compounds Our data suggest that the chalate EGCG inhibits GABA activation and shares the α + β − GABA-binding pocket. Campbell et al. have also shown that EGCG blocks GABA activation [28]. In their detailed study, they show that the block is underpinned by an increase in the GABA EC 50 . Taking these observations together, we hypothesize that the α + β − interface contains a functional GABA binding site that can be targeted by the competitive antagonists clarithromycin and EGCG. The GABA antagonist actions of these compounds are consistent with their use in humans, where they are both associated with increased arousal [9,10,29].
The convulsant pentylenetetrazole is a well-known GABA A R blocker [46]. Its site and mechanism of action are equivocal. Early studies suggested an overlapping binding site with benzodiazepines [47]. This view changed to the general consensus that pentylenetetrazol is a channel blocker [48,49], and that it shares a transmembrane binding site with picrotoxin, which has now been resolved using cryo-electron microscopy [45]. However, the PTZ binding site consensus has been elegantly challenged [50], suggesting while there may be some overlap between the mechanisms of PTX and PTZ, the sites are distinct. Our findings support this latter finding. Using microfluidic patch clamp, we were able to demonstrate that PTZ was an effective inhibitor of GABA activation, and our modeling studies suggest that it does not prefer binding in the M2 2 -9 region of the channel, instead docking favorably in the orphan γ + β − binding site. Interestingly, our structural models suggest that this site can also bind to the modest inhibitors metronidazole and hydroxychloroqine, and the irreversible blocker minocycline (data not shown).
Our findings have broader implication for the study of neuropharmacology and the treatment of patients with arousal disorders. In rat pyramidal neurons, clarithromycin has been shown to induce hyperexcitability, increase the firing rate, and reduce the amplitude of spontaneous miniature inhibitory postsynaptic GABAergic currents [51]. Our data strongly support these findings. Together, our work and previous studies suggest a mechanism of action for clarithromycin reversing sleepiness in patients suffering from idiopathic hypersomnia. In this population, clarithromycin has been shown to improve subjective measures of sleep [9]. A recent meta-analysis of the available clinical data continues to favor the use of clarithromycin in the treatment of hypersomnia in the majority of cases [52].
Our clinical experience led us to investigate clarithromycin as a potential treatment for idiopathic hypersomnia, and as a potential novel GABA A RNAM. Since clarithromycin is not well-tolerated by many patients (the side effects include metallic taste, nausea, and headache), we sought to test other macrolides and antibiotics to see if they may also be useful in treating hypersomnia via a GABAergic mechanism. We were further motivated in this effort by the previous research into GABA A R modulation by antibiotics from several drug classes, since many therapeutics used to treat infections are associated with disturbances in arousal [53,54]. For example, the β-lactam penicillin, is known to be a GABA A R channel blocker [55], and quinolone antibiotics inhibit GABA receptor activation [56]. We were therefore hopeful that other macrolides and/or tetracyclines, ansamycins, etc., may demonstrate activity at the GABA A receptor, and may represent additional therapeutic options for patients with poorly controlled excessive daytime sleepiness.
Our results with the quinolones chloroquine and hydroxychloroqine are consistent with this class of compounds having modest action at GABA A Rs. However, the prediction that the two chemically similar compounds prefer different binding pockets was not anticipated. Our studies indicate that the β − γ + pocket can accommodate a diverse array of molecules, including hydroxychloroquine, yet chloroquine appears to prefer docking to the γ − α + pocket.
The promiscuous nature of the interactions predicted here is interesting and has been explored previously [57]. GABA A Rs contain a well-characterized agonist, GABAPAM and GABA A RNAM binding sites. The GABA and benzodiazepine agonist sites are found on either side of the alpha subunits (α − and α + , respectively) while low-affinity GABAPAMs are thought to act at cavities between the helical transmembrane segments [45,58,59]. GABA A RNAMs seem to predominantly locate to the channel lumen. Our findings, while in part supporting this simple model, suggest that multiple sites, some novel, may play a role in receptor modulation and the side effects produced by the drugs studies here.
The clinical relevance of our findings are notable. With one exception (EGCG), the CNS concentration of the modulator is at least 37% of the GABA IC 50 . Put another way, during normal therapeutic use, the compounds investigated here will have a significant effect on receptor activation in the CNS. Given the safety profile of these compounds, our findings suggest that some of these compounds might be useful in treating excessive sleepiness.
Finally, our studies have demonstrated that microfluidic patch clamp methods are a suitable substitute to conventional methods for screening GABA A receptor modulators. We found that the microfluidic approach had several advantages over conventional methods. First, the apparatus is straightforward to use, and unlike conventional methods, it does not require prolonged training. Second, the miniaturized set-up means that drug or biological fluid use is reduced by 2-10-fold per data point. This is especially useful if screening small volumes of human samples, expensive therapeutics, or screening difficultto-synthesize compounds. In fact, while each determination may need only 50 uL of drug to set up the assay, after the experiment, 90% may be reclaimed for subsequent analysis. Third, the 384-well plate format facilitates the collection of large datasets (2000-4000 data episodes/hour). Finally, since the ensemble plate allows us to record from multiple cells simultaneously, this reduces the impact of cell-cell EC 50 variability. This is especially useful when studying allosteric modulators where strict adherence to a fixed reference of agonist is a critical part of the experimental design.

Conclusions
The compounds tested in this study are structurally diverse, yet they all modulate GABA A receptor activation and can impair arousal in humans. From a structure-activity perspective, this may seem counterintuitive. Our modeling data provide an insight into this paradox, and show how the GABA A receptor can act as a nexus for these compounds by binding them at multiple sites that in turn, impair receptor activation, translating the binding of disparate molecular structures into changes in a common pathway.
Guided by the side effects associated with the therapeutics studies here, we have developed a pipeline of clinically safe compounds that may be useful in treating the symptoms of pathological sleepiness. These findings may benefit patients diagnosed with Idiopathic Hypersomnia, Type 1 Myotonic Dystrophy, or Kleine-Levin Syndrome, who experience excessive daytime sleepiness that is not controlled with conventional stimulants. Future work in human subjects and with human CSF samples are needed to determine the mechanisms of action and the therapeutic potential of these compounds.